Deep residual features are extracted from the output of the last convolutional block of a 50-layer deep residual network (ResNet-50) [1]} that is pre-trained on ImageNet. Figure REF  shows the architecture of the ResNet-50 deep network which we have used for feature extraction. The ResNet-50 is made up of five convolutional blocks stacked on top of each other (Figure REF ). The convolutional blocks of a ResNet are different from those of the traditional CNNs because of the introduction of a shortcut connection between the input and output of each block. Identity mappings when used as shortcut connections in ResNets [2]}, can lead to better optimization and reduced complexity. This in turn allows one to use deeper ResNets which are faster to train and are computationally less expensive than the conventional CNNs i.e., VGGnet [3]}.
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